As for the visual representations, we apply the pretrained ResNet-10 [1]} to extract the visual features. The hyperparameter \(\lambda \)  is set to \( 0.1 \) . For the outer task, we employ Adam optimizer with initial learning rate with \( 0.0001 \) . And for the inner task, we apply SGD optimizer with learning rate with \( 0.001 \) . \(\lambda \)  of the inner loss is 0.1. We utilize the most popular settings in few-shot learning: 5-way 1-shot and 5-way 5-shot settings. The number of query set is 15 per class.
